Cyclical weight decay introduces a new hyper-parameter, \(f_c\) , in addition to the user-defined \(WD_{min}\)  and \(WD_{max}\) . Figure REF  shows the test accuracies from training with a range of values for \(f_c\)  in CWD on ImageNet with a TResNet_m architecture [1]}. The Figure compares the accuracies when using two different weight decay ranges and the shapes of these curves imply different optimal values for \(f_c\) . Actually, these results are mostly within the precision of our experiments, implying that the performance is relatively insensitive to the value of \(f_c\) . In Table REF , we used a value of \(f_c = 4\)  for training with CWD.
